Clustering algorithms allow the grouping of similar regions in an image. Clustering is usually achieved by defining regions, in which neighboring voxels have similar values. These voxels are then combined, forming a cluster, see D. L. Pham et al.: “Current Methods in Medical Imaging”, Annu. Rev. Biomed. Eng. 2000. 02:315-37. A cluster map therefore reduces the quasi-continuous values of the original image to a smaller number of levels, forming a cluster map. This is depicted in FIG. 1 showing an example of three cluster levels, cluster level A, B and C. The resulting maps can be displayed alone or overlaid over the original topographic data, see A. T. Agoston et al.: “Intensity-modulated parametric mapping for simultaneous display of rapid dynamic and high-spatial-resolution breast MR imaging data”, Imaging and Therapeutic Technology 21, 217, 2001. Cluster maps are used for various applications, one prominent and important example being radio therapy planning (RTP), see L. Xing et al.: “Inverse planning for functional image-guided intensity modulated radiation therapy”, Phys. Med. Biol. 47, 3567, 2002.
Achieving a cluster map by a simple and basic clustering algorithm such as K-means algorithm usually results in fragmented clusters marked by the dotted circle in FIG. 1 101, and isolated clusters 102-104.
In applications such as RTP, segmentation and isolation is a major problem. For effective dose planning it is necessary to reduce the number of cluster areas to a minimum, avoiding both segmented and isolated clusters. Several morphological segmentation algorithms exist to achieve this goal, such as “erosion” and “dilation”, distance transformation, see Milan Sonka and J. Michael Fitzpatrick: Handbook of Medical Imaging, Volume 2. FIG. 2 shows a “reduced cluster map”, resulting from the cluster map in FIG. 1 after appliance of suitable clustering algorithms. FIG. 2 contains only one single cluster level C resulting after merging of fragmented clusters 101 from FIG. 1 into a single cluster, and erasing the isolated clusters.
Various approaches and algorithms exist to accomplish the described reduction of a cluster map, and therefore different results can be achieved. Since such a reduction of the cluster map always results in loss of initial image information, it is important to evaluate the executed modifications. Especially in the medical environment it is crucial to have access to a powerful and yet simple evaluation tool. On one side, therapy planning must not be based on misarranged cluster data, on the other side complicated methods will not find acceptance in a clinical environment.